PROJECT SUMMARY Otitis media is one of the most common childhood diseases in developing countries; many of its complications are preventable if middle ear fluid is detected early. We propose an accessible and accurate smartphone-based screening tool that (i) sends a soft acoustic chirp into the ear canal using the smartphone speaker, (ii) detects reflected sound from the eardrum using the smartphone microphone, and (iii) employs a machine learning model to classify these reflections and predict middle ear fluid status in realtime. Given the ubiquity of smartphones and the inaccuracy of visual otoscopy, the system we propose has the potential to be the default screening tool used in developing countries by healthcare providers and caregivers at home.